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Record W7112189997

“Freakin swimming and everythink”: Variable (ING) in youth Aboriginal English

2024· article· en· W7112189997 on OpenAlexaboutno aff

Bibliographic record

VenueUWA Profiles and Research Repository (UWA) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)Variation (astronomy)MainstreamVernacularScholarshipNorth American EnglishVoice-onset timeBritish EnglishRealisation
DOInot available

Abstract

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Freakin swimming and everythink: Variable (ING) in youth Aboriginal English<br/><br/>Fraiese, Lucía; Collard, Glenys; Rodríguez Louro, Celeste; Walker, James; Gardner, Matt Hunt<br/><br/>Variable (ING) in English has been recognised for its long-term stability. While most scholarship has focused on mainstream (Cole, 2020; Forrest &amp; Wolfram, 2019; Hazen, 2008; Horvath, 1985) and racialized Englishes (e.g., Kendall &amp; Thomas, 2019; Van Hofwegen &amp; Wolfram, 2010), less in known about its use in Indigenised Englishes (but see Ball &amp; Bernhardt, 2008; Mulder, 1982; Sterzuk, 2003). (ING) variation has been argued to be a vernacular universal (Chambers, 2004), yet how it patterns in Indigenised Englishes is less well understood. In this paper, we consider Australian Aboriginal English (AE), a post-invasion contact-based variety of English spoken by 80% of First Nations people in Australia (Author). While frequent use of the alveolar variant has been reported by Malcolm (2018), quantitative sociolinguistic studies of variable (ING) as used in AE are yet to be offered.<br/><br/>We draw on two original synchronic corpora of naturalistic talk-in-interaction spoken AE data collected in Nyungar boodja, southwest Western Australia (Author). In line with the literature (e.g., Horvath, 1985; Kendall &amp; Thomas, 2019; Travis, Grama &amp; Purser, 2023; Author), all tokens of variable (ING) (shown in examples 1-3) were extracted and coded impressionistically for their phonetic realisation (alveolar, velar, stopped voiced and stopped voiceless), and for grammatical category, phonological context, and syllable count. The social constraints included speaker year of birth, gender, and proximity of hometown to a major city. A preliminary sample of 13 AE speakers born between 2001 and 2010 (6 women and 7 men) reveals significant interspeaker variation, but the rates of use of the alveolar variant are overall higher than those found in other varieties of English in Australia (as reported in, e.g., Travis et al., 2023; Author). Gender is a significant social predictor, with males leading in the use of the alveolar variant, and females showing an overall higher rate of stopping. Place of residence and grammatical category also interact with phonetic realisation, suggesting that those who reside in major cities have higher rates of the alveolar variant overall, and show a distinction between verbal and non-verbal forms. Those who hail from remote areas, however, do not display such a distinction, and show higher rates of velar realisations, which are still significantly lower than those found in other mainstream varieties of English (Author). These results confirm observations by Malcolm (2018) of the presence of the alveolar variant in AE. The presence of stopping among females, although also attested elsewhere (Gordon, 1998), makes an interesting case for further exploration.<br/><br/>Examples<br/><br/>1. I got real annoyed when you mob sayin that. (Alessa, woman, b. 2010)<br/>2. And then we was just um, like, I don’t know, freakin swimming and everythink. (Frankie, woman, b. 2007)<br/>3. This fellow was like coming close to us. (RWC, man, b. 2001)<br/><br/>References<br/><br/>Ball, Jessica &amp; Bernhardt, B. May (2008). First Nations English dialects in Canada: Implications for speech-language pathology: Indigenous and Colonial Languages: Implications for Speech-Language Pathology Research and Practice. Clinical linguistics &amp; phonetics 22(8): 570-588.<br/>Chambers, Jack K. (2004). Dynamic typology and vernacular universals. In Kortmann, B. (Ed.), Dialectology meets typology dialect grammar from a cross-linguistic perspective. Berlin: Mouton de Gruyter.<br/>Cole, Amanda (2020). Co-variation, style and social meaning: The implicational relationship between (h) and (ing) in Debden, Essex. Language variation and change 32(3): 349-371.<br/>Forrest, Jon &amp; Wolfram, Walt (2019). The Status Of (ING) in African American Language: A Quantitative Analysis of Social Factors and Internal Constraints. American speech 94(1): 72-90.<br/>Gordon, Elizabeth (1998). Anythink or Nothink: A Lazy Variant or an Ancient Treasure? New Zealand English journal 12(12): 25-33.<br/>Hazen, Kirk (2008). (ING): A vernacular baseline for English in Appalachia. American speech 83(2): 116-140.<br/>Horvath, Barbara M. (1985). Variation in Australian English: the sociolects of Sydney. Cambridge [Cambridgeshire]: Cambridge University Press.<br/>Kendall, Tyler S. &amp; Thomas, Erik R. (2019). Variable (ING). In Thomas, E. R. (Ed.), Mexican American English: Substrate Influence and the Birth of an Ethnolect. Cambridge: Cambridge University Press. 171-197.<br/>Malcolm, Ian (2018). Australian Aboriginal English: Change and continuity in an adopted language. Boston: De Gruyter.<br/>Mulder, Jane (1982). The Tsimshian English dialect: the result of language interference. In Bartelt, G. H., Penfield-Jasper, S. &amp; Hoffer, B. (Eds.), Essays in Native American English. San Antonio, TX: Trinity University Press. 95–112.<br/>Sterzuk, Andrea (2003). A study of Indigenous English speakers in the Standard English classroom. ProQuest Dissertations Publishing.<br/>Travis, Catherine E., Grama, James &amp; Purser, Benjamin (2023). Stability and change in (ing): Ethnic and grammatical variation over time in Australian English. English world-wide 44(3): 435-469.<br/>Van Hofwegen, Janneke &amp; Wolfram, Walt (2010). Coming of age in African American English: A longitudinal study. Journal of sociolinguistics 14(4): 427-455.<br/><br/><br/>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.370
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
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